Daiki Takeuchi
Impact in
- Signal Processing top 5%
- Speech and Audio Processing
- Music and Audio Processing
- Blind Source Separation Techniques
- Artificial Intelligence top 10%
- Speech Recognition and Synthesis
- Anomaly Detection Techniques and Applications
Papers in
-
- Speech and Audio Processing 20
- Music and Audio Processing 19
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- Speech Recognition and Synthesis 12
- Natural Language Processing Techniques 3
- Co-authors
- Noboru Harada (26 shared papers)Kohei Yatabe (8 shared papers)Yuma Koizumi (5 shared papers)Daisuke Niizumi (19 shared papers)Yasunori Ohishi (17 shared papers)Kunio Kashino (9 shared papers)Marc Delcroix (2 shared papers)Yoshiki Masuyama (1 shared paper)
In The Last Decade
Daiki Takeuchi
29 papers receiving 373 citations
Peers
Comparison fields: 5 of 68
- Signal Processing 262
- Artificial Intelligence 161
- Music 9
- Computational Mechanics 55
- Computer Vision and Pattern Recognition 53
Countries citing papers authored by Daiki Takeuchi
This map shows the geographic impact of Daiki Takeuchi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daiki Takeuchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daiki Takeuchi more than expected).
Fields of papers citing papers by Daiki Takeuchi
This network shows the impact of papers produced by Daiki Takeuchi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daiki Takeuchi. The network helps show where Daiki Takeuchi may publish in the future.
Co-authors
The 25 scholars most cited alongside Daiki Takeuchi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 95 | |
| 2 | 2022 | 42 | |
| 3 | 2024 | 38 | |
| 4 | 2020 | 35 | |
| 5 | 2023 | 26 | |
| 6 | 2023 | 19 | |
| 7 | 2024 | 19 | |
| 8 | 2019 | 15 | |
| 9 | 2022 | 15 | |
| 10 | 2019 | 10 | |
| 11 | 2022 | 9 | |
| 12 | 2021 | 9 | |
| 13 | 2020 | 8 | |
| 14 | 2024 | 6 | |
| 15 | 2020 | 6 | |
| 16 | 2024 | 6 | |
| 17 | 2018 | 5 | |
| 18 | 2024 | 5 | |
| 19 | 2011 | 4 | |
| 20 | 2023 | 3 |
About Daiki Takeuchi
Daiki Takeuchi is a scholar working on Signal Processing, Artificial Intelligence, Music, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 34 papers that have together received 391 indexed citations. Recurring topics across this work include Speech and Audio Processing (20 papers), Music and Audio Processing (19 papers), Speech Recognition and Synthesis (12 papers), Advanced Adaptive Filtering Techniques (4 papers), Natural Language Processing Techniques (3 papers), Music Technology and Sound Studies (3 papers), Diverse Musicological Studies (3 papers) and Hearing Loss and Rehabilitation (3 papers). The work is most often cited by research in Signal Processing (262 citations), Artificial Intelligence (161 citations), Music (9 citations), Computational Mechanics (55 citations) and Computer Vision and Pattern Recognition (53 citations). Daiki Takeuchi has collaborated with scholars based in Japan and China. Frequent co-authors include Noboru Harada, Kohei Yatabe, Yuma Koizumi, Daisuke Niizumi, Yasunori Ohishi, Kunio Kashino, Marc Delcroix, Yoshiki Masuyama, Yasuhiro Oikawa and Masahiro Yasuda. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, The Journal of the Acoustical Society of America, Cell Genomics, IEICE Transactions on Electronics and Optics Express.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.